Convolution neural network is successful in pervasive vision tasks, including label distribution learning, which usually takes the form of learning an injection from the non-linear visual features to the well-defined labels. However, how the discrepancy between features is mapped to the label discrepancy is ambient, and its correctness is not guaranteed.To address these problems, we study the mathematical connection between feature and its label, presenting a general and simple framework for label distribution learning. We propose a so-called Triangular Distribution Transform (TDT) to build an injective function between feature and label, guaranteeing that any symmetric feature discrepancy linearly reflects the difference between labels. The proposed TDT can be used as a plug-in in mainstream backbone networks to address different label distribution learning tasks. Experiments on Facial Age Recognition, Illumination Chromaticity Estimation, and Aesthetics assessment show that TDT achieves on-par or better results than the prior arts.

翻译：卷积神经网络在包括标签分布学习在内的广泛视觉任务中取得了成功，这类任务通常采用从非线性视觉特征到精确定义标签的映射学习形式。然而，特征差异映射到标签差异的机制尚不明确，且其正确性无法保证。针对这些问题，我们研究了特征与标签之间的数学联系，提出了一种通用且简单的标签分布学习框架。我们提出名为三角分布变换（TDT）的方法，构建特征与标签间的单射函数，确保任何对称特征差异能线性反映标签差异。所提出的TDT可作为主流骨干网络的即插即用模块，应用于不同标签分布学习任务。在面部年龄识别、光照色度估计和美学评估实验表明，TDT取得了与现有技术相当或更优的结果。